Imaging applications such as synthetic aperture radar (SAR) create two-dimensional maps (bitmaps) of regions, where the images consist of a two-dimensional grid of different signal strengths (magnitudes). These grids break down further into regions of interest (ROIs, also called chips), that is, regions where there is something other than background being imaged, such as objects or possible targets (objects of interest). Chips break down into clusters, that is, sets of pixels having a particular property, such as a similar signal magnitude. The chips are processed to reveal any objects (e.g., cars, trees, buildings, etc.) that the imaging application detected as well as any objects of interest (also known as targets, and include predefined objects such as tanks, planes, etc.). Automated target recognition (ATR) is a process that identifies targets based on a template database.
ATR uses target images for input. Distinct target images—that is, each target separately identified and differentiated from background (clutter) or other objects—enhance ATR reliability. The differentiation of objects from background, of objects of interest (targets) from other objects, and of targets from other targets are thus important, but often difficult, processes.
There is a need for improved separation of closely spaced targets in a ROI for applications such as SAR. Improved target recognition results in improved ATR.